AI Engineer CV — example, template & tips for India 2026
Build an ATS-optimised AI engineer CV that showcases LLM application development, RAG pipelines, and production AI agents — not just model training. For GenAI roles at AI-first startups, product companies, and enterprise AI teams across India.
Salary range (India)
₹8L – ₹65L+ depending on production GenAI experience and company tier
Top hiring companies
Sarvam AI · Krutrim · Fractal Analytics · Amazon AI · Microsoft AI
What to include in a AI Engineer CV
- LLM application work: which foundation models you have built with (GPT-4o, Claude, Gemini, Llama, Mistral) and via which APIs or hosting (OpenAI, Anthropic, Bedrock, Vertex AI, self-hosted)
- RAG (Retrieval-Augmented Generation) systems: vector databases used (Pinecone, Weaviate, Qdrant, pgvector), chunking strategy, and retrieval accuracy improvements
- Agentic systems: frameworks used (LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen) and what autonomous tasks the agent performs end-to-end
- Prompt engineering rigour: versioned prompts, evaluation harnesses, and measurable output-quality improvements (not just "wrote good prompts")
- Production concerns specific to GenAI: latency/cost trade-offs across models, hallucination mitigation, guardrails, and token-usage optimisation
- Fine-tuning or evaluation experience if applicable: LoRA/QLoRA, RLHF, or structured eval frameworks (Ragas, promptfoo, custom LLM-as-judge pipelines)
Common AI Engineer CV mistakes
- Confusing "used ChatGPT for tasks" with genuine AI engineering — this role is about building systems that use LLMs, not personal productivity use
- No mention of RAG or vector databases — in 2026 this is the single most-screened AI engineering keyword after the model names themselves
- Missing cost and latency context — GenAI hiring managers care deeply about token cost per request and p95 latency, not just accuracy
- Overlapping entirely with a classical ML engineer CV — if you have not touched LLMs, prompting, or RAG directly, target the Machine Learning Engineer path instead
- No evaluation methodology — "the agent worked well" is weak; "evaluation harness scored 92% task success across 200 test cases" is strong
- Ignoring guardrails and safety — omitting hallucination mitigation, PII redaction, or content filtering signals inexperience with production GenAI risk
Top skills for a AI Engineer CV
These keywords are heavily screened in ATS for ai engineer roles. Make sure they appear naturally in your CV.
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Check my keyword score freeAI Engineer CV — frequently asked questions
What is the difference between an AI engineer and a machine learning engineer CV in India?
An AI engineer in the 2026 job market overwhelmingly means someone who builds applications on top of large language models: RAG pipelines, AI agents, chatbots, and copilots using APIs like OpenAI, Anthropic, or Bedrock, plus frameworks like LangChain and LlamaIndex. A machine learning engineer more often trains, fine-tunes, and deploys custom models (classification, recommendation, forecasting) using PyTorch/TensorFlow and owns the MLOps pipeline around them. The two roles increasingly overlap — many "AI engineer" JDs now also expect fine-tuning knowledge — but if your recent work is primarily prompting, retrieval, and orchestrating LLM calls rather than training models from data, position your CV as an AI engineer, and lead with RAG/agent/LLM-API keywords rather than classical ML metrics.
Do I need a machine learning background to become an AI engineer in India?
A strong software engineering background with Python is the higher-leverage prerequisite; deep ML theory (backpropagation, loss functions) is helpful but not mandatory for most GenAI application roles in 2026. What Indian AI-first companies and product teams actually screen for is: comfort building with LLM APIs, understanding of RAG architecture and vector search, prompt engineering discipline, and the ability to ship reliable, cost-aware production systems that call external model APIs. Many successful AI engineers in India moved from backend or full-stack roles by building 2–3 substantial GenAI side projects (a RAG chatbot, an agentic workflow tool) and documenting the architecture and evaluation results clearly on their CV and GitHub.
Which vector database should I mention on my AI engineer CV?
List whichever you have hands-on production or project experience with — Pinecone and Weaviate are the most recognised managed options in Indian JDs, pgvector is common where teams already run PostgreSQL, and Qdrant and Milvus appear frequently in cost-sensitive or self-hosted setups. What matters more than the specific database is showing you understand the trade-offs: chunking strategy, embedding model choice, hybrid search (keyword + vector), and how you measured retrieval quality (recall@k, manual relevance review, or an automated eval set). Naming one database with that depth outperforms listing four without context.
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